Methods and apparatus for localizing a source of a set of radio signals

ABSTRACT

Methods and apparatus are provided for localizing a source of a set of radio signals, such as radio signals received from an RFID tag at various locations. A source of a set of radio signals (such as radio signals received from an RFID tag at various locations) is localized by obtaining a plurality of radio signals in the set from a different location in an environment; determining a magnitude and received location for each of the plurality of radio signals; determining a direction for each of the plurality of radio signals by comparing each given radio signal to other radio signals in the set; and determining a location of the source of the set of radio signals by determining an intersection of the direction for each of the plurality of radio signals. The direction for each of the plurality of radio signals optionally comprises a net directional vector determined using a weighted circular mean.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/668,215, filed Mar. 25, 2015, incorporated by reference herein.

FIELD OF THE INVENTION

Embodiments of the invention generally relate to information technology,and, more particularly, to techniques for localizing a source of a setof radio signals.

BACKGROUND

Data centers are facilities used to house computer systems and relateddevices, such as power supplies, communications and storage systems,environmental controls (such as air conditioning and fire suppression)and various security devices. A number of techniques have been proposedor suggested for automatically navigating, mapping and/or monitoringdata centers. For example, J. Lenchner et al., “Towards Data CenterSelf-Diagnosis Using a Mobile Robot,” ACM Int'l Conf. on AutonomicComputing (ICAC '11) (2011), incorporated by reference herein, disclosesa robot that serves as a physical autonomic element to automaticallynavigate, map and monitor data centers. The disclosed robot navigates adata center, mapping its layout and monitoring its temperature and otherquantities of interest with little, if any, human assistance. Inaddition, U.S. patent application Ser. No. 13/348,846, filed Jan. 12,2012 (now U.S. Pat. No. 9,606,542), entitled “Discovery and Monitoringof an Environment Using a Plurality of Robots,” incorporated byreference herein, discloses techniques for coordinating multiple mobilerobots for exploring and monitoring a given environment or region.

Radio Frequency IDentification Identification (RFID) tags are often usedin numerous industrial applications as a method for ready identificationof a given asset. An asset is first tagged with a given RFID tag and theassociation is commonly stored in an asset database. Typically, activeRFID tags broadcast their respective serial number on a periodic basisand a receiver then records the received serial number along with thesignal strength of the received signal and the location at which thesignal was received. While RFID tags have greatly improved the abilityto readily track equipment, they suffer from a number of limitations,which if overcome, could further extend the utility and efficiency ofsystems that either automatically or semi-automatically track equipment.Localizing the assets to a desired granularity is often quite difficultdue to, for example, multipath propagation of signals and directionalinterference.

A need therefore remains for improved techniques for localizing a sourceof a set of radio signals, such as radio signals received from an RFIDtag at various locations.

SUMMARY

In one aspect of the present invention, methods and apparatus areprovided for localizing a source of a set of radio signals, such asradio signals received from an RFID tag at various locations. Anexemplary computer implemented method for localizing a source of a setof radio signals (such as radio signals received from an RFID tag atvarious locations) comprises the steps of obtaining a plurality of radiosignals in the set from a different location in an environment, whereineach of the plurality of radio signals is transmitted by the source;determining a magnitude and received location for each of the pluralityof radio signals; determining a direction for each of the plurality ofradio signals by comparing each given radio signal to other radiosignals in the set; and determining a location of the source of the setof radio signals by determining an intersection of the direction foreach of the plurality of radio signals, wherein at least one of thesteps are performed by at least one hardware device.

In one exemplary implementation, the direction for each of the pluralityof radio signals comprises a net directional vector that is determinedusing a weighted circular mean. The location of the source of the set ofradio signals can be determined, for example, by identifying anintersection of the net directional vector for each of the plurality ofradio signals.

Another aspect of the invention or elements thereof can be implementedin the form of an article of manufacture tangibly embodying computerreadable instructions which, when implemented, cause a computer to carryout a plurality of method steps, as described herein. Furthermore,another aspect of the invention or elements thereof can be implementedin the form of an apparatus including a memory and at least oneprocessor that is coupled to the memory and configured to perform notedmethod steps. Yet further, another aspect of the invention or elementsthereof can be implemented in the form of means for carrying out themethod steps described herein, or elements thereof; the means caninclude hardware module(s) or a combination of hardware and softwaremodules, wherein the software modules are stored in a tangible computerreadable storage medium (or multiple such media).

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary indoor environment in which the presentinvention can be employed;

FIG. 2 is a flow chart illustrating an exemplary asset tracking processincorporating aspects of the present invention;

FIG. 3 illustrates a signal strength map comprising a plurality ofsignal strength measurements obtained from a particular RFID tag atvarious locations in the exemplary indoor environment of FIG. 1;

FIG. 4 illustrates an exemplary processing of an exemplary subset of thesignal strength measurements shown in FIG. 3;

FIG. 5 illustrates a vector field of the signal strength measurementsobtained from a particular RFID tag in the exemplary indoor environmentof FIG. 1;

FIG. 6 illustrates an exemplary computation of the intersection of thearcs in accordance with an exemplary flood-fill technique;

FIG. 7 illustrates a determination of the location of a particular RFIDtag based on the intersection of the arcs of FIG. 6;

FIGS. 8A through 8C, collectively, comprise exemplary pseudo code forperforming the exemplary asset tracking process of FIG. 2; and

FIG. 9 is a system diagram of an exemplary computer system on which atleast one embodiment of the invention can be implemented.

DETAILED DESCRIPTION

Aspects of the present invention provide methods, apparatus and computerprogram products that localize a source of a set of radio signals, suchas radio signals received from an RFID tag at various locations. Whilean exemplary embodiment of the present invention is illustrated usingthe localization of equipment in data centers using RFID signals, thepresent invention can be applied to any electromagnetic signaldistribution mechanism where the location and received signal strengthare recorded, as would be apparent to a person of ordinary skill in theart.

The term “building,” as used herein, is intended to refer to a varietyof facilities, including, but not limited to, data centers hosting largeamounts of information technology (IT) equipment, as well as industrialoffice space and residential buildings.

FIG. 1 illustrates an exemplary indoor environment 100 in which thepresent invention can be employed. Let one or more robots {R₁, . . . ,R_(k)} be given and suppose that the space to be explored within theexemplary indoor environment 100 has been discretized into a set ofsquare “tiles.” In some practical environments, such as computer datacenters, the natural discretization unit is in fact a physical floor orceiling tile. In other environments, the discretization unit may bevirtual tiles. The exemplary indoor environment 100 of FIG. 1 comprisesan exemplary array of 6-by-6 tiles, being explored by one exemplaryrobot R₁. Tiles filled with a cross-hatched pattern indicate thepresence of obstacles. As discussed further below, the exemplary robotR₁ navigates through the exemplary indoor environment 100 and recordsreceived serial numbers from one or more RFID tags along with thestrength of the received signal and location of the robot when thesignal was received.

For a detailed discussion of suitable exemplary robots, see, forexample, U.S. patent application Ser. No. 12/892,532, filed Sep. 28,2010, entitled “Detecting Energy and Environmental Leaks in IndoorEnvironments Using a Mobile Robot,” incorporated by reference herein.The term “robot,” as used herein, refers generally to any form of mobileelectro-mechanical device that can be controlled by electronic orcomputer programming. In this basic form, as will be described in detailbelow, the exemplary robots move throughout the designated portions ofthe exemplary indoor environment 100 and take signal measurements aswell as positioning data (so as to permit the signal strength data to beassociated with a given position in the building 100). The robots shouldbe capable of moving in various directions along the floor of thebuilding, so as to navigate where the robots need to go and to maneuveraround obstacles, such as equipment, furniture, walls, etc., in thebuilding 100.

It is preferable that the exemplary robot (e.g., R₁) have the capabilityto collect and store the data, i.e., serial number, signal strength andpositioning data, to allow for analysis at a later time though it isalso possible that the data be streamed to a controlling or servercomputer where the data may be processed and/or stored. As discussedhereinafter, the exemplary indoor environment 100 can be a known orunknown environment.

Aspects of the present invention recognize that RF signal strength doesnot monotonically decrease with distance from the RFID tag. In fact,RFID tags can exhibit variable signal strength profiles when measuredfrom precisely the same location in the exemplary indoor environment 100at different times. In addition, indoor environments, such as theexemplary indoor environment 100, typically suffer from multi-pathingissues, especially in the presence of metal surfaces.

It can be shown that due to interference, impedance effects can cause asignal to appear to have originated further from a point of detectionthan the actual location. Non-monotonicity manifests itself due to avariable amount of interference. In addition, RF signal strengths arenot consistent from tag to tag. For example, assume that a first tag isplaced at a given location (x,y) and that its signal strength (asmeasured by the Received Signal Strength Indication, or RSSI, aninteger-valued indicator of signal strength that decreases withincreasing signal strength, though the exact manner and calibration ofthis decrease may vary by manufacturer) is then sampled from 10 otherlocations. A second tag may then be placed at the same location as thefirst tag, and RSSI readings taken at the same locations from which thefirst tag was measured. The first tag may have a RSSI distribution from60 (strong) to 75 (weak) while the second tag may have a RSSIdistribution of 70 (strong) to 90 (weak). Thus, the first tag'scalibration, or correlation of RSSI to a distance, may not be consistentwith that of the second tag.

Further, range specific models, to the extent that they have someapplicability, inherently require range calibration or an initialsampling of data to get a characteristic function relating signalstrength to estimated distance from the source of the signal.

One exemplary embodiment of the invention assumes that a signal map isprovided, which plots the strength of a signal at various locationsaround the exemplary indoor environment 100. In one exemplaryimplementation, the exemplary robot R₁ is outfitted with an RFCodeactive RFID reader that fuses the beaconing signals with the currentlocation of the robot R₁. In an alternative implementation, a readercould be moved around an environment, leaving the reader in variouslocations for a minimum beacon interval, then moving the reader toanother location until a signal map is obtained.

FIG. 2 is a flow chart illustrating an exemplary asset tracking process200 incorporating aspects of the present invention. Generally, theexemplary asset tracking process 200 compares each measured signal of agiven RFID tag to every other signal from the given RFID tag todetermine a “direction” inferred by each measured signal. In thismanner, the exemplary asset tracking process 200 determines trends inthe location of each measured signal and infers the “direction” of eachmeasured signal.

In one exemplary embodiment, stronger signals are given more weight incomputing the direction than weaker signals. Once directions for eachmeasured signal for the given RFID tag are computed, angular arcs areprojected. The intersection of the arcs is the estimated location of thegiven RFID tag.

As shown in FIG. 2, the exemplary asset tracking process 200 initiallycaptures tag data throughout the indoor environment 100 during step 210,as discussed further below in conjuction with FIG. 3. In one exemplaryimplementation, the data is captured using a robot R₁ during a routineexploration. The exemplary data is filtered for a given RFID tag (byserial number) and comprises position information (e.g., X, Y) andsignal strength (often reported as RSSI). As noted above, a lower RSSIvalue indicates a stronger signal.

Thereafter, the exemplary asset tracking process 200 computes thedirection for each measured signal during step 220, as discussed furtherbelow in conjuction with FIGS. 4 and 5. In this manner, for every signalreceived from a given tag, a directional component is computed based onall other measured signals for the given tag using a weighted circularmean.

The uncertainty associated with these vectors gives rise to an arcduring step 230 and the intersection among the respective arcs iscomputed using a flood-fill technique, as discussed further below inconjunction with FIGS. 6 and 7. For example, after every directionalcomponent is computed for each signal received from a tag, an arc isthen created that is +/−x degrees about that directional component,where the magnitude of x may, for example, be taken to be inverselyproportional to the standard deviation of the signal strengths used todetermine the unit directional vector, or, alternatively, to some fixedvalue.

The exemplary flood-fill technique discretizes the map into arectangular grid of n equally spaced nodes. The spacing of the nodes isdetermined by the required resolution of the approximation. To improveaccuracy, the node size can be decreased thereby increasing the numberof nodes at the cost of additional computation time. The nodes containedinside each of these arcs are identified by using an exemplary seedingand flood-filling technique. Once these nodes are identified, they aregiven a weight based on the strength of the signal in question.

In one exemplary embodiment, the approximation is improved using thelayout obtained by or known to the robot R₁, for example, to localizethe tag to the nearest object or closest front-of-rack. The node on themap with the maximum number of intersecting arcs nearest to the closestfront-of-rack is then the estimated location of the tag.

FIG. 3 illustrates a signal strength map 300 comprising a plurality ofsignal strength measurements obtained from a particular RFID tag atvarious locations in the exemplary indoor environment 100 of FIG. 1. Aspreviously noted, the exemplary signal strength map 300 is generated byan autonomous data center robot R₁ having an RFID reader. In theexemplary embodiment, lower RSSI values indicate stronger RFID signals(i.e., a value of 60 is stronger than a value of 80). The exemplaryasset tracking process 200 processes the signal strength map 300 tolocalize the given RFID tag. The signal strength map 300 comprises asubset 310 of the signal strength measurements that is discussed furtherbelow in conjunction with FIG. 4, for a particular region of theexemplary indoor environment 100.

FIG. 4 illustrates an exemplary processing of the subset 310 of thesignal strength measurements shown in FIG. 3. As shown in FIG. 4, thesubset 310 of the signal strength measurements comprises an exemplarynode at which the system has received a signal for a given tag and atwhich the system needs to compute a directional vector. This node isreferred to herein as the SIGNAL-TO-COMPUTE-VECTOR-FOR 410. AtSIGNAL-TO-COMPUTE-VECTOR-FOR 410, the robot R₁ has received an exemplarysignal strength measurement of 80. It has also received two additionalsignals from the same tag in the immediate vicinity, referred to asADDITIONAL-SIGNAL_1, 420-1, and ADDITIONAL-SIGNAL_2, 420-2, havingexemplary signal strength measurements of 65 and 73, respectively. Theexemplary SIGNAL-TO-COMPUTE-VECTOR-FOR 410 is the current signal whosenet directional vector is being computed. The exemplaryADDITIONAL-SIGNAL_i's (e.g., 420-1 and 420-2) are the signals that arecompared to the SIGNAL-TO-COMPUTE-VECTOR-FOR 410. As previouslymentioned, the value of 80 is the “weakest” signal. This algorithm willthen only compare a SIGNAL-TO-COMPUTE-VECTOR-FOR to stronger signals(which generally have less error).

It is noted that the two ADDITIONAL-SIGNAL_is 420-1 and 420-2, areapproximately equally spaced from the exemplarySIGNAL-TO-COMPUTE-VECTOR-FOR 410. Thus, if equal weighting were applied,a net direction vector 430 for the exemplarySIGNAL-TO-COMPUTE-VECTOR-FOR 410 would point along the +x axis, from theweaker signal to the stronger signal. Generally, the exemplaryembodiment employs a weighting where the higher the difference betweenthe SIGNAL-TO-COMPUTE-VECTOR-FOR 410 and a given ADDITIONAL-SIGNAL_i420, the greater the influence on the net direction vector 430. Forexample, since the difference between the exemplary ADDITIONAL-SIGNAL_1420-1 and the exemplary SIGNAL-TO-COMPUTE-VECTOR-FOR 410 is greater thanthe difference between the exemplary ADDITIONAL-SIGNAL_2 420-2 and theexemplary SIGNAL-TO-COMPUTE-VECTOR-FOR 410, the vector between theexemplary ADDITIONAL-SIGNAL_1 420-1 and the exemplarySIGNAL-TO-COMPUTE-VECTOR-FOR 410 has more influence on the net directionvector 430. Note, when evaluating each signal, only the signals that arestronger than the exemplary SIGNAL-TO-COMPUTE-VECTOR-FOR 410 areconsidered in the exemplary implementation, as opposed to the weakersignals. This is due to the fact that weaker signals are generally moreprevalent and can overly influence the computed net direction.Furthermore, weaker signals may contain a significantly larger amount oferror due to multipathing issues and general signal impedance.

The net directional vector 430 is computed for each measured signal incomparison to all other received stronger signals for a given RFID tag.In the exemplary embodiment, a weighted circular mean is computed usingthe following equation:ā=a tan 2((Σ_(j=1) ^(n) sin(a)_(j) *w _(j))*(Σ_(j=1) ^(n) cos(a)_(j) *w_(j)))

It is noted that a tan 2(y component, x component) is a standardtwo-value arctan function that returns an angle in the appropriatequadrant of a unit circle (see, e.g.,http://en.wikipedia.org/wiki/Atan2). The variable ā is the resultantangle for a given signal location that corresponds to the netdirectional vector 430 of interest. The variable a_(J) is an anglebetween the SIGNAL-TO-COMPUTE-VECTOR-FOR 410 (the center of ahypothetical circle) and a particular ADDITIONAL-SIGNAL_j, 420-1 or420-2. The variable a_(J) is obtained by computing a tan2(ADDITIONAL-SIGNAL_j.y−SIGNAL-TO-COMPUTE-VECTOR-FOR.y,ADDITIONAL-SIGNAL_j.x−SIGNAL-TO-COMPUTE-VECTOR-FOR.x).

The variable w_(J) is a weight computed by obtaining the differencebetween the SIGNAL-TO-COMPUTE-VECTOR-FOR 410 and a particularADDITIONAL-SIGNAL_i 420. Generally, small differences correspond to alow weight (because small differences provide little information).

For a given RFID measured signal (SIGNAL-TO-COMPUTE-VECTOR-FOR 410),iterate through all of the other measured signals (ADDITIONAL-SIGNAL_j420), computing the value a_(J) for each ADDITIONAL-SIGNAL_j 420. Whenthe value a_(J) is computed for a SIGNAL-TO-COMPUTE-VECTOR-FOR 410 andADDITIONAL-SIGNAL_j 420, the weight w_(J) is also computed for that pairof signals and the results are fed into the above equation for ā, thusobtaining the circular mean.

FIG. 5 illustrates an exemplary vector field 500 of the signal strengthmeasurements obtained from a particular RFID tag in the exemplary indoorenvironment 100 of FIG. 1. The pointer associated with each measuredsignal is based on an inferred “direction” determined from each measuredsignal. It is noted that not all pointers point directly at theestimated tag location 510. In this manner, from every measured signal,the direction of a given RFID tag is estimated. The exception to thiscase is the strongest measured signal since there are no strongersignals to compare it to. Since all other vectors are based off of thestrongest signal, the vector of the strongest signal can be omitted.

The consensus pointing direction and location of the vectors in thevector field 500 are computed during step 230 of the exemplary assettracking process 200 by creating an arc from the vectors and using aflood-fill technique, as shown in FIGS. 6 and 7. Rather than calculatingthe intersection of rays, the exemplary embodiment treats each vector asan arc and finds all of the nodes contained in those arcs. Arcs are usedto accomodate uncertainty in the measurements i.e., in one embodimentthe fewer the number of signals used to determine the net directionvector results, the wider the arc.

FIG. 6 illustrates an exemplary computation of the consensus pointingdirection and estimated location of the beaconing RFID tag in accordancewith an exemplary flood-fill technique. As shown in FIG. 6, theexemplary flood-fill technique starts with a seed node 610 selectedalong the direction of the net direction vector 430 or a node that iscontained in the arc and then evaluates all connected child nodescontained in the arc. The variable α may be taken to be a constant(e.g., 20 degrees) but may also vary, as previously noted, based on theuncertainty in the measurements giving rise to a particular arc.

The arc is defined by the +/− alpha degrees about the net directionvector 430 as previously calculated. Then, starting from a nodecontained in the arc, iterate through all of the child nodes testingeach node for being contained in the arc. If the node is successfully inthe arc, then the children of the node are also checked until this arcis completely filled and there are no more nodes to consider.

FIG. 7 illustrates the determination of the location of a particularRFID tag based on the intersection of the arcs of FIG. 6. Generally,after all of the arcs are computed and the number of intersections ateach node has been summed, as discussed above in conjunction with FIG.6, the point with the maximum number of intersections (i.e., peak 710)is the estimated location of the RFID tag. If there are multiple nodesthat share the peak, then the centroid is the estimated location.

FIGS. 8A through 8C, collectively, comprise exemplary pseudo code 800,820, 840 for performing the exemplary asset tracking process 200 of FIG.2. Generally, the exemplary pseudo code 800, 820, 840 determines thenodes that are inside an arc using an exemplary flood-fill technique. Asshown in FIG. 8A, the process is seeded with a point contained inside ofthe arc, and then verifies each point by ensuring that the angle betweena signal point and a node in the question angle is within bounds. Theindividual verification step of each possible node is removed.

As shown in FIG. 8B, the process reduces the significance of weakersignals (e.g., higher RSSI value), since they are more prevalent. StrongSIGNAL-TO-COMPUTE-VECTOR-FOR signals 410 are given a higher weight(e.g., more votes). In addition, the exemplary flood-fill technique isinitiated in FIG. 8B and concludes in FIG. 8C. The point of maximumintersection (see, FIG. 7) is found by iterating through the map 500(FIG. 5). The “shape” of the peak 710 generated can be analyzed foradditional information. For example, in one exemplary embodiment, if thepeak 710 is wider, then a lower confidence score can be assigned.Likewise, if the peak 710 is more narrow, then a higher confidence scorecan be assigned.

Among other benefits, the exemplary asset tracking process 200 does notuse range based estimation. In addition, the exemplary asset trackingprocess 200 can handle tags outside of the convex hull of the sampledregion. Further, the exemplary asset tracking process 200 does notrequire pre-calibration, previously known data, a selective searchpattern for a given tag, or pre-filtering of data.

The techniques depicted in FIGS. 2 and 8A-8C can also, as describedherein, include providing a system, wherein the system includes distinctsoftware modules, each of the distinct software modules being embodiedon a tangible computer readable recordable storage medium. All of themodules (or any subset thereof) can be on the same medium, or each canbe on a different medium, for example. The modules can include any orall of the components shown in the figures and/or described herein. Inan aspect of the invention, the modules can run, for example, on ahardware processor. The method steps can then be carried out using thedistinct software modules of the system, as described above, executingon a hardware processor. Further, a computer program product can includea tangible computer readable recordable storage medium with code adaptedto be executed to carry out at least one method step described herein,including the provision of the system with the distinct softwaremodules.

Additionally, the techniques depicted in FIGS. 2 and 8A-8C can beimplemented via a computer program product that can include computeruseable program code that is stored in a computer readable storagemedium in a data processing system, and wherein the computer useableprogram code was downloaded over a network from a remote data processingsystem. Also, in an aspect of the invention, the computer programproduct can include computer useable program code that is stored in acomputer readable storage medium in a server data processing system, andwherein the computer useable program code is downloaded over a networkto a remote data processing system for use in a computer readablestorage medium with the remote system.

An aspect of the invention or elements thereof can be implemented in theform of an apparatus including a memory and at least one processor thatis coupled to the memory and configured to perform exemplary methodsteps.

Additionally, an aspect of the present invention can make use ofsoftware running on a general purpose computer or workstation. Withreference to FIG. 9, such an implementation might employ, for example, aprocessor 902, a memory 904, and an input/output interface formed, forexample, by a display 906 and a keyboard 908. The term “processor” asused herein is intended to include any processing device, such as, forexample, one that includes a CPU (central processing unit) and/or otherforms of processing circuitry. Further, the term “processor” may referto more than one individual processor. The term “memory” is intended toinclude memory associated with a processor or CPU, such as, for example,RAM (random access memory), ROM (read only memory), a fixed memorydevice (for example, hard drive), a removable memory device (forexample, diskette), a flash memory and the like. In addition, the phrase“input/output interface” as used herein, is intended to include, forexample, a mechanism for inputting data to the processing unit (forexample, mouse), and a mechanism for providing results associated withthe processing unit (for example, printer). The processor 902, memory904, and input/output interface such as display 906 and keyboard 908 canbe interconnected, for example, via bus 910 as part of a data processingunit 912. Suitable interconnections, for example via bus 910, can alsobe provided to a network interface 914, such as a network card, whichcan be provided to interface with a computer network, and to a mediainterface 916, such as a diskette or CD-ROM drive, which can be providedto interface with media 918.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in associated memory devices (for example, ROM, fixed orremovable memory) and, when ready to be utilized, loaded in part or inwhole (for example, into RAM) and implemented by a CPU. Such softwarecould include, but is not limited to, firmware, resident software,microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 902 coupled directly orindirectly to memory elements 904 through a system bus 910. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including but not limited to keyboards 908,displays 906, pointing devices, and the like) can be coupled to thesystem either directly (such as via bus 910) or through intervening I/Ocontrollers (omitted for clarity).

Network adapters such as network interface 914 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modems andEthernet cards are just a few of the currently available types ofnetwork adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 912 as shown in FIG. 9)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method and/or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, as noted herein, aspects of the present invention may takethe form of a computer program product that may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (for example, lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the components detailed herein. Themethod steps can then be carried out using the distinct software modulesand/or sub-modules of the system, as described above, executing on ahardware processor 902. Further, a computer program product can includea computer readable storage medium with code adapted to be implementedto carry out at least one method step described herein, including theprovision of the system with the distinct software modules.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof, for example, application specific integratedcircuit(s) (ASICS), functional circuitry, an appropriately programmedgeneral purpose digital computer with associated memory, and the like.Given the teachings of the invention provided herein, one of ordinaryskill in the related art will be able to contemplate otherimplementations of the components of the invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition ofanother feature, integer, step, operation, element, component, and/orgroup thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed.

At least one aspect of the present invention may provide a beneficialeffect such as, for example, controlling the activities of a systemadministrator or another user on an endpoint device.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for localizing a source of a set ofradio signals, the method comprising the steps of: obtaining a pluralityof radio signals in said set by moving a signal reader to a plurality oflocations in an environment, wherein each of said plurality of radiosignals is transmitted by said source; determining one or more signalstrengths of said radio signals, a location at which said plurality ofradio signals are obtained, and an identifier of said source;determining a directional vector for each of said plurality of radiosignals by comparing said one or more signal strengths to signalstrengths of other radio signals in said set; projecting said determineddirectional vectors; and determining a location of said source of saidset of radio signals using an intersection selection criterion thatevaluates a number of said projected determined directional vectors thatintersect for each of said plurality of radio signals, wherein at leastone of said steps are performed by at least one hardware device.
 2. Themethod of claim 1, wherein said set of radio signals comprises radiosignals received from an RFID tag by moving said signal reader to saidplurality of locations.
 3. The method of claim 1, wherein said signalstrength of each given radio signal in said set is proportional to areceived signal strength at said corresponding received location of saidenvironment.
 4. The method of claim 1, wherein said signal strength ofeach of said plurality of radio signals in said set is proportional to afunction of a received signal strength at said corresponding receivedlocation of said environment.
 5. The method of claim 1, wherein saiddirectional vector for each of said plurality of radio signals comprisesa net directional vector.
 6. The method of claim 5, wherein said netdirectional vector is determined using a weighted circular mean.
 7. Themethod of claim 1, wherein said intersection is identified by creatingone or more arcs and employing a flood-fill technique.
 8. The method ofclaim 7, wherein said location of said source is identified bydetermining a substantially largest number of said intersections.